English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose Efficient Neural Architecture Search (ENAS), a fast andinexpensive approach for automatic model design. In ENAS, a controller learnsto discover neural network architectures by searching for an optimal subgraphwithin a large computational graph. The controller is trained with policygradient to select a subgraph that maximizes the expected reward on thevalidation set. Meanwhile the model corresponding to the selected subgraph istrained to minimize a canonical cross entropy loss. Thanks to parameter sharingbetween child models, ENAS is fast: it delivers strong empirical performancesusing much fewer GPU-hours than all existing automatic model design approaches,and notably, 1000x less expensive than standard Neural Architecture Search. Onthe Penn Treebank dataset, ENAS discovers a novel architecture that achieves atest perplexity of 55.8, establishing a new state-of-the-art among all methodswithout post-training processing. On the CIFAR-10 dataset, ENAS designs novelarchitectures that achieve a test error of 2.89%, which is on par with NASNet(Zoph et al., 2018), whose test error is 2.65%.

Efficient Neural Architecture Search via Parameter Sharing